Data usage recommendation generator

ABSTRACT

The method includes receiving, by one or more computer processors, a request for data usage, the request for data usage being associated with a mobile device. The method further includes identifying, by one or more computer processors, a current data usage quantity, the current data usage quantity being associated with the mobile device. The method further includes determining, by one or more computer processors, whether the current data usage quantity is beyond a threshold data usage. The method further includes responsive to the data usage quantity being beyond the threshold data usage, receiving, by one or more computer processors, mobile device information associated with the mobile device. The method further includes analyzing, by one or more computer processors, the mobile device information. The method further includes determining, by one or more computer processors, access for the request based on the mobile device information and the current data usage quantity.

BACKGROUND

The present invention relates generally to the field of cellularnetworks and more particularly to data usage.

A cellular network or mobile network is a communication network wherethe last link is wireless. Mobile and other computing devices accesscellular networks in order to perform actions and functions, such asaccessing the Internet, communicating, or viewing content. When acomputing device accesses a cellular network the computing device usesdata. Providers of cellular networks often restrict the amount of data aparticular computing device can use during any period of time. Users ofcomputing devices may have difficulty monitoring data usage to avoidexceeding these data usage thresholds.

SUMMARY

Aspects of the present invention disclose a method, computer programproduct, and system for determining data usage access. The methodinclude receiving, by one or more computer processors, a request fordata usage, the request for data usage being associated with a mobiledevice. The method further includes identifying, by one or more computerprocessors, a current data usage quantity, the current data usagequantity being associated with the mobile device. The method furtherincludes determining, by one or more computer processors, whether thecurrent data usage quantity is beyond a threshold data usage. The methodfurther includes responding to the data usage quantity being beyond thethreshold data usage, by receiving, by one or more computer processors,mobile device information associated with the mobile device. The methodfurther includes analyzing, by one or more computer processors, themobile device information. The method further includes determining, byone or more computer processors, access for the request based on themobile device information and the current data usage quantity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a computing environment suitable foroperation of a data access program, in accordance with at least oneembodiment of the present invention;

FIG. 2 is a flowchart depicting operational steps for a data accessprogram, in accordance with at least one embodiment of the presentinvention; and

FIG. 3 is a block diagram of components of a computing apparatussuitable for executing a data access program, in accordance with atleast one embodiment of the present invention.

DETAILED DESCRIPTION

FIG. 1 is a block diagram illustrating a distributed data processingenvironment, generally designated 100, in accordance with one embodimentof the present invention. FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made by those skilledin the art without departing from the scope of the invention.

The distributed data processing environment 100 includes a computingdevice 112 communicating with a network 110. The network 110 represents,for example, a telecommunications network, a local area network (LAN), awide area network (WAN), such as the Internet, or a combination of thethree, and includes wires, wireless, and/or fiber optic connections.Network 110 includes one or more wired and/or wireless networks that arecapable of receiving and transmitting data, voice, and/or video signals,including multimedia signals that include voice, data, and videoinformation.

In the depicted embodiment, the computing device 112 is one or more of amanagement server, a web server, a client device, or any otherelectronic device or computing system capable of receiving, generating,and sending data. In some embodiments, the computing device 112 is acomputing system utilizing multiple computers as a server and/or clientsystem, such as in a cloud computing environment. In some embodiments,the computing device 112 is a laptop, a tablet computer, a netbookcomputer, a personal computer (PC), a desktop computer, a personaldigital assistant (PDA), a smart phone, or any programmable electronicdevice capable of communicating with another computing device (notdepicted) via the network 110. In other embodiments, the computingdevice 112 is a computing system utilizing clustered computers andcomponents to act as a single pool of seamless resources. The computingdevice 112 may include components as depicted and described in furtherdetail with respect to FIG. 3, in accordance with embodiments of thepresent invention. The computing device 112 includes a data accessprogram 120, a database 130, and an analytics engine 124.

The analytics engine 124 is a software program capable of receivinginput, generating results, and communicating the results with the dataaccess program 120. In some embodiments, the analytics engine 124 is anengine for cognitive computing. In some embodiments, the analyticsengine 124 is a general purpose analytics engine. In other embodiments,the analytics engine 124 operates on a model tailored to data accessprograms (e.g., data access program 120). The analytics engine 124 maybe configured for finding and analyzing patterns and efficiencies at themacro level, understood in the context of data usage and access systems.In various embodiments, the analytics engine 124 may be a function ofdata access program 120. In an example, data access program 120 may havemultiple functions or models, one of which may be the analytics engine124.

In the depicted embodiment, the analytics engine 124 resides on thecomputing device 112. In other embodiments, the analytics engine 124 mayreside elsewhere in the distributed data processing environment 100,such as within another computing device (not illustrated), a serverdevice (not illustrated), or a client device (not illustrated). In someembodiments, the data access program 120 is the analytics engine 124.

In the depicted embodiment, the database 130 resides on the computingdevice 112. In another embodiment, the database 130 may reside elsewherein the distributed data processing environment 100, such as withinanother computing device, a server device, a client device, or as astandalone database that is capable of communicating with the computingdevice 112 via the network 110. The database 130 is an organizedcollection of data. The Database 130 is implemented with any type ofstorage device capable of storing data that is accessed and utilized bythe computing device 112, such as a database server, a hard disk drive,or a flash memory. In some embodiments, the database 130 representsmultiple storage devices within the computing device 112. The database130 stores information related to the computing device 112, such as thecomputing device 112's data usage history, data usage plan, data usagelimit, application usage, location, times associated with activity, userpreset threshold data limits, and other information relevant to theusage of the computing device 112, such as availability of the network110, type of connection available via the network 110, and signalstrength of the network 110.

In the depicted embodiment, the data access program 120 resides on thecomputing device 112. In another embodiment, the data access program 120may reside elsewhere in the distributed data processing environment 100,such as within another computing device, a server device, or a clientdevice that is capable of communicating with the computing device 112via the network 110. In some embodiments, the data access program 120receives information from the computing device 112, the database 130,and the analytics engine 124. In some embodiments, the data accessprogram 120 may reside on a server device and communicate with acorresponding data access program client device (not illustrated). Insuch an embodiment, the data access program client device may reside ona computing device, such as the computing device 112. In such anembodiment, the data access program 120 may receive information from thedata access program client device. For example, the data access program120 may reside on a server device and receive information, such as userhistory, data usage, and other client device information, from anapplication on a cellular phone.

FIG. 2 is a flowchart depicting operational steps for the data accessprogram 120, in accordance with at least one embodiment of the presentinvention. In some embodiments, the operational steps for the dataaccess program 120 are initiated when a computing device is turned on.In other embodiments, the operational steps for the data access programare initiated when the data access program 120 receives a request fordata usage associated with a computing device. In some embodiments, theoperational steps for the data access program 120 may be initiatedresponsive to input from a user.

At step 200, the data access program 120 receives a request for datausage associated with a mobile device, such as the computing device 112.Data usage refers to digital information the computing device 112 useswhen accessing the cellular network. Actions requiring the computingdevice 112 to access a cellular network require the computing device 112to use data. Data usage may be measured in units of digital information,such as bytes. Certain actions use more data than other actions. Forexample, streaming a video requires the computing device 112 to use moredata than making a phone call. In some embodiments, each time thecomputing device 112 begins performing an action that uses data, thedata access program 120 may receive the request for data usage. Forexample, the computing device 112 receives a request for data usage whenthe computing device 112 opens an email or a social media application.The request for data usage may include meta-information about therequest. For example, the request for data usage may include a timeassociated with when the request is made, a location associated withwhere the request is made, or the type of request being made.

At step 202, the data access program 120 identifies data usageassociated with the mobile device. The data usage is associated with thecomputing device 112. Data usage for the computing device 112 may bemeasured and/or tracked as a total for the lifespan of the device, inreal time, or incremental totals (e.g., monthly, weekly, periodically,etc.). In some embodiments, the data access program 120 may identify thedata usage within a database, such as the database 130. In someembodiments, the database 130 may store data information for thecomputing device 112 incrementally. For example, a data usage for thecomputing device 112 may be determined every week, and a log of datausage may be stored within the databased 130. In some embodiments, thedatabase 130 may store a record of each action performed by thecomputing device 112 and the data usage associated with each of theactions. In such an embodiment, the data access program 120 maycalculate the data usage by adding the data usage stored within thedatabase 130. In such an embodiment, the data access program 120 maycalculate the data usage by adding the data usage stored within thedatabase 130 for a particular time period, such as for a current monthor for a current billing period. In some embodiments, the data accessprogram 120 may identify data usage from a user profile. For example, auser may set up a profile that identifies different applications he orshe uses. In such an example, the data access program 120 may identifydata usage from the profile.

At step 204, the data access program 120 determines whether the datausage is beyond a threshold data usage. The threshold data usage may bepredetermined based on a subscription plan. For example, a user of amobile phone may pay ten dollars a month for one gigabyte of data usage.In such an embodiment, one gigabyte of data usage per month is thethreshold data usage. In other embodiments, a user may predetermine thethreshold. The threshold data usage may be stored in a database that iscapable of communicating with the data access program 120, such as thedatabase 130. The data access program 120 may determine whether the datausage is beyond the threshold data usage by comparing the two values tosee which has a greater value. If, no, the data usage is not beyond thethreshold data usage, the data access program 120 exits. If, yes, thedata usage is beyond the threshold data usage, the data access program120 proceeds to step 206.

In some embodiments, the data access program 120 may determine thethreshold data usage based on information stored within the database130. In some embodiments the analytics engine 124 may determine thethreshold data usage based on information stored within the database130. For example, the analytics engine 124 may determine the thresholddata usage based on a predetermined amount set by a subscription plan inaddition to mobile data use history. For example, if a subscription plansets a limit at twenty gigabytes of data and a user has a historyutilizing large amounts of data in a short period of time (e.g.,streaming videos) the threshold data usage may be that 18 gigabytes areallotted. If, however, the user has a history of only utilizing smallincrements of data over time (e.g., 0.1 gigabytes per day) the thresholddata usage may be 19.8 gigabytes.

In some embodiments, the threshold data usage is based on a percentageof usage of a total data usage for a billing cycle of a wirelesssubscription plan. For example, a subscription plan may set a twentygigabytes of data limit for a billing cycle. In such an example, thethreshold data usage may be ninety percent of the twenty gigabytes ofdata limit.

At step 206, the data access program 120 receives mobile deviceinformation associated with the mobile device. The mobile deviceinformation is associated with the computing device 112 and may bestored within the database 130. The mobile device information includesat least one artefact selected from the group consisting of: atimestamp, a location of a mobile device, a current data usage, a datausage history of the mobile device, a signal strength of the mobiledevice, a network connection availability of the mobile device, and amobile device application usage trail.

The computing device information may include information about time, alocation for the computing device 112, current data usage for thecomputing device 112, data usage associated with the request, strengthof the network 110's signal(s), type of the network 110's signals(wireless, cellular, etc.), a log of data usage history for thecomputing device 112, or a mobile device application usage trail.

Information about time may be a timestamp for when the request is made.Information about time may also include timestamps for similar requests.For example, information about time may be that the computing device 112always accesses a social media website at or around 4 p.m. Informationabout time may also be how long a data using action has lasted in thepast. For example, information about time may be that the computingdevice 112 typically spends ten minutes checking email or two hoursstreaming videos.

Information about location may be a location for where the request ismade. For example, location information may be that the computing device112 is at a particular address. Location information may also includepredetermined and/or pre-identified frequent locations. For example, auser of the computing device 112 may indicate that a specific locationis his or her home or his or her place of employment. Locationinformation may include that the location where the request is beingmade is a new location, or one the computing device 112 has never beento before. Location information may include information about thelocation, such as whether the location is a restaurant, a shoppingcenter, a hospital, etc.

Current data usage for the computing device 112 may be associated withthe data usage threshold. For example, current data usage may be theamount of data that has been used for the current month, period, orbilling cycle. Current data usage may also include information about thetype of data that has been used over the past month. For example,whether data was used to make phone calls, download videos, etc.

Data usage associated with the request from step 200 may be based onprevious actions taken by the computing device 112. For example, if therequest is to access a social media website, and the computing device112 typically uses one megabyte of data each time the social mediawebsite is accessed, the data usage associated with the request toaccess the social media website may be one megabyte of data. In otherembodiments, data usage associated with the request may vary based ontime spent performing the action. For example, if the request is tostream episodes of a television show, information about the request maybe that one hour of streaming will use one megabyte of data and twohours of streaming will use two megabytes of data, etc. In such anexample, the data access program 120 may indicate to a user of thecomputing device 112 information indicating that the data used for therequest will vary based on the time spent performing the action.

Strength of the network signal may include information about how easilythe computing device 112 is able to access the network 110. For example,in certain areas, access to a network is difficult based on proximity ofavailable cellular towers and networks. In such an example, strength ofnetwork signal may include a measurement of current signal strength.Strength of signal information may also include current locationinformation. For example, the current signal strength may be low due toweather at the current location, or due to the current location being atunnel.

Type of network signal available may include information about the typeof network the network 110 provides the computing device 112. In someembodiments, the network 110 provides multiple possible connections,such as a wireless or a cellular signal. Type of network signalavailable may include current location information. For example, thetype of network signal available may include that wireless access isrestricted for the computing device 112 or that wireless access isavailable at a nearby location.

A mobile device application usage trail is a grouping of commands,programs, and functions that have historically occurred together or inproximity. For example, a mobile device application usage trail mayinclude that using a camera application is shortly followed by using anediting software within a social media website.

At step 208, the data access program 120 analyzes the mobile deviceinformation. In some embodiments, the analysis involves the data accessprogram 120 sending computing device information to the analytics engine124, the analytics engine 124 analyzing the computing deviceinformation, and the data access program 120 receiving analyzedcomputing device information.

The analytics engine 124 may analyze the computing device informationbased on a static and/or dynamic process correlating the requestreceived at step 200. For example, the analytics engine 124 may be a bigdata analytics engine. Big data analytics is a process of analyzing andexamining large volumes of data to discover patterns, correlations, andother information about the data. Big data analytics analyzes highervolumes of data than conventional analytics and business intelligencesolutions would analyze. For example, the big data analytics engine mayaccess two hundred million pages of structured and unstructured contentconsuming over four terabytes of disk storage. Big data analytics mayinclude predictive analytics. Predictive analytics encompassesstatistical techniques such as predictive modeling, machine learning,and data mining to analyze current and historical facts to makepredictions about future, or otherwise unknown, events. The analyticsengine 124 may identify patterns, correlation, and trends within thecomputing device information. The analytics engine 124 may use thepatterns, correlations, and trends within the computing deviceinformation to extrapolate or predict further actions based on therequest and the computing device information. The analytics engine 124may analyze the computing device information using high-performance datamining, predictive analytics, machine learning, natural languageprocessing, text mining, text analysis, forecasting, and/oroptimization.

For example, the analytics engine 124 may analyze time information todetermine a pattern of data accessing behavior. For example, theanalytics engine 124 may identify that a user checks his or her emailaround 8:00 a.m. every morning. In such an example the analytics engine124 may identify the user checking his or her email around 8:00 a.m.every morning as a pattern of behavior.

In another example, the analytics engine 124 may analyze mobile deviceinformation to determine how much data a particular request willrequire. For example, the analytics engine 124 may use information aboutuser history to determine how much data a particular request willrequire. In such an example, the analytics engine 124 may determine if auser typically streams videos for several hours at a time usingapproximately five gigabytes of data each time, the analytics engine 124may determine that a request for streaming a video will use fivegigabytes of data. In another example the analytics engine 124 may useinformation about location. For example, the analytics engine 124 mayidentify that at certain locations a request uses more data than thesame request would at another location due to the poor quality ofnetwork cellular reception. In another example, the analytics engine 124may use time information. In such an example, the analytics engine 124may identify the late afternoon as a high traffic time for a particularcellular phone application, and that the high traffic slows the serverfor the particular cellular phone application, in turn causing a requestto use more data than that same request would at an earlier or latertime.

In another example, the analytics engine may 124 analyze informationabout location. For example, the analytics engine 124 may identify thata first time a user travels to a location, he or she accesses a map onhis or her computing device, but does not use a map during subsequentvisits. The analytics engine 124 may also identify that a user downloadsa weather report for a location approximately one hour before leavingtowards the location. In such an example, the analytics engine 124 mayidentify a conditional pattern of behavior such that if a user downloadsa weather report for a location the user has not previously visited theuser will access a map in approximately an hour.

In some embodiments, the analytics engine 124 may identify thesepatterns based on input from a computer programmer. For example, acomputer programmer may identify potential patterns for the analyticsengine 124 to look for. In other embodiments, the analytics engine maycompare information from regular time intervals, such as daily orweekly, looking for similarities and differences, and/or compareinformation from different locations looking for similarities anddifferences.

At step 210, the data access program 120 determines access for therequest. The request was received at step 200. The access is whether ornot, and the manner by which, the computing device 112 should performthe request for data usage. In some embodiments, access is allowing therequest. In other embodiments, access is prohibiting the request.

In some embodiments, access is based on whether or not a user would berequired to purchase additional data as a part of his or hersubscription plan. For example, access may always be prohibited ifadditional data must be purchased to perform a request. In anotherembodiment, access may be prohibited unless a user is in a new locationand downloading a map. In another embodiment, access may be prohibitedunless a user manually overrides the prohibition.

In other embodiments, access is deferring the request. For example, thedata access program 120 may identify a time for the request indicating anew billing cycle within a monthly subscription plan is about to begin.In such an example, the data access program 120 may determine that therequest should be deferred. In another embodiment, the data accessprogram 120 may identify a location for the computing device 112 andidentify that the computing device 112 is near a location with access toa wireless network (e.g., one block from home) and defer the requestuntil the computing device 112 is at the location with access to thewireless network. In another example, the data access program 120 mayidentify that a user will enter a new billing cycle in one day and deferthe request until the user and the computing device 112 are in a newbilling cycle.

In another example, the data access program 120 may defer access to asocial media site because the data access program 120 has determined thesocial media site is a lower priority activity (as opposed to makingphone calls, accessing maps, or checking emails). Such a prioritizationmay be based on user input, input from a program developer, or a featureof the data access program 120. For example, the data access program 120may identify certain activities as having a higher priority based on thetime of day the activities are typically performed. For example,activities performed between the hours of 8:30 a.m. and 12:00 p.m. maybe ranked as having a higher priority than activities performed betweenthe hours of 12:00 p.m. and 1:00 p.m. Deferring the request may be doneso that the request is completed at a future time such as when thecomputing device 112 has access to a wireless network.

In other embodiments, access includes congesting the request. Congestingthe request may include making adjustments to the manner in which therequest is carried out such that the action uses less data. For example,the request may be to view a video via a web browser within thecomputing device 112. The data access program 120 may display the videowith a lower quality. In another example, the request may be to view aweb page with images and graphics embedded within the web page. The dataaccess program 120 may act by displaying the web page without the imagesand graphics such that the web page only contains plain text. Congestingthe request may be done so that the request uses less data than it wouldotherwise use. The data access program 120 may analyze previous requestssimilar to the request for data usage and compare data usage betweenrequests of lower quality or with fewer images.

In other embodiments, access is prefetching the request. The data accessprogram 120 may identify within the data usage history that a computingdevice 112 uses several social media websites during a morning commute.In such an embodiment, the data access program 120 may download contentfor the social media websites when the computing device 112 has accessto a wireless network, such as when the computing device 112 is at homeor at a train station. In the previous example, where a user downloadsweather information one hour before downloading map information if theuser has never been to the location, prefetching the request may bedownloading the map at a time earlier than when the user attempts todownload the map, such as while the weather information is beingdownloaded if the computing device 112 has access to a wireless networkat the time the weather information is downloaded. Prefetching therequest may be done so that the request and/or a predicted futurerequest does not use data since the request was completed when thecomputing device 112 had access to a wireless network.

In some embodiments, the data access program 120 has access to functionsand applications within the computing device 112. In such embodiments,the data access program 120 may allow access based on determinationsmade at step 204 automatically. For example, the data access program 120may automatically download a video with lower quality or pre-fetchcontent from a social media website. In some embodiments, the dataaccess program 120 may generate an alert indicating the type of accessfor a particular request and display the alert via the computing device112.

FIG. 3 is a block diagram depicting components of a computer 300suitable for executing the data access program 120. In some embodimentsthe computer 300 is the computing device 112. FIG. 3 displays thecomputer 300, the one or more processor(s) 304 (including one or morecomputer processors), the communications fabric 302, the memory 306, theRAM 316, the cache 316, the persistent storage 308, the communicationsunit 310, the I/O interfaces 312, the display 320, and the externaldevices 318. It should be appreciated that FIG. 3 provides only anillustration of one embodiment and does not imply any limitations withregard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environment may be made.

As depicted, the computer 300 operates over a communications fabric 302,which provides communications between the cache 316, the computerprocessor(s) 304, the memory 306, the persistent storage 308, thecommunications unit 310, and the input/output (I/O) interface(s) 312.The communications fabric 302 may be implemented with any architecturesuitable for passing data and/or control information between theprocessors 304 (e.g., microprocessors, communications processors, andnetwork processors, etc.), the memory 306, the external devices 318, andany other hardware components within a system. For example, thecommunications fabric 302 may be implemented with one or more buses or acrossbar switch.

The memory 306 and persistent storage 308 are computer readable storagemedia. In the depicted embodiment, the memory 306 includes a randomaccess memory (RAM). In general, the memory 306 may include any suitablevolatile or non-volatile implementations of one or more computerreadable storage media. The cache 316 is a fast memory that enhances theperformance of computer processor(s) 304 by holding recently accesseddata, and data near accessed data, from memory 306.

Program instructions for the data access program 120 may be stored inthe persistent storage 308 or in memory 306, or more generally, anycomputer readable storage media, for execution by one or more of therespective computer processors 304 via the cache 316. The persistentstorage 308 may include a magnetic hard disk drive. Alternatively, or inaddition to a magnetic hard disk drive, the persistent storage 308 mayinclude, a solid state hard disk drive, a semiconductor storage device,read-only memory (ROM), electronically erasable programmable read-onlymemory (EEPROM), flash memory, or any other computer readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by the persistent storage 308 may also be removable. Forexample, a removable hard drive may be used for persistent storage 308.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of the persistentstorage 308.

The communications unit 310, in these examples, provides forcommunications with other data processing systems or devices. In theseexamples, the communications unit 310 may include one or more networkinterface cards. The communications unit 310 may provide communicationsthrough the use of either or both physical and wireless communicationslinks. The data access program 120 may be downloaded to the persistentstorage 308 through the communications unit 310. In the context of someembodiments of the present invention, the source of the various inputdata may be physically remote to the computer 300 such that the inputdata may be received and the output similarly transmitted via thecommunications unit 310.

The I/O interface(s) 312 allows for input and output of data with otherdevices that may operate in conjunction with the computer 300. Forexample, the I/O interface 312 may provide a connection to the externaldevices 318, which may include a keyboard, keypad, a touch screen,and/or some other suitable input devices. External devices 318 may alsoinclude portable computer readable storage media, for example, thumbdrives, portable optical or magnetic disks, and memory cards. Softwareand data used to practice embodiments of the present invention may bestored on such portable computer readable storage media and may beloaded onto the persistent storage 308 via the I/O interface(s) 312. TheI/O interface(s) 312 may similarly connect to a display 320. The display320 provides a mechanism to display data to a user and may be, forexample, a computer monitor.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A system for determining data usage access, comprising: a mobile device configured to request access to data; a database configured to store data usage quantity for the mobile device; an analytics engine configured to analyze mobile device information associated with the mobile device, wherein the mobile device information is received in response to a data usage quantity exceeding a threshold; and a data access program, the data access program configured to: identify a current data usage quantity, the current data usage quantity associated with the mobile device; determine whether the current data usage quantity is beyond a threshold data usage; and determine an access based on the mobile device information and the current data usage quantity, wherein the access is determined without further interaction from a user.
 2. The system of claim 1, wherein the mobile device information comprises at least one artefact selected from the group consisting of: (a) a timestamp; (b) a location of the mobile device; (c) the current data usage; (d) a data usage history of the mobile device; (e) a signal strength of the mobile device; (f) a network connection availability of the mobile device; and (g) a mobile device application usage trail.
 3. The system of claim 1, wherein the access is selected from the group consisting of: (a) allowing the request; (b) prohibiting the request; (c) deferring the request; and (d) congesting the request.
 4. The system of claim 1, wherein the threshold data usage is based on a percentage of usage of a total data usage allotted for a billing cycle according to a wireless subscription plan.
 5. The system of claim 1, wherein the analytics engine if further configured to analyze the mobile device information based on mobile device information utilizing big data analytics.
 6. The system of claim 1, wherein the access comprises prefetching, the request when the mobile device is utilizing wireless connectivity.
 7. The system of claim 6, wherein prefetching, by one or more computer processors, the request comprises: receiving, by the one or more computer processors, data usage history; analyzing, by the analytics engine, the data usage history to yield a pattern of data usage activity; and downloading, by the one or more computer processors, content based on the pattern of data usage activity prior to receiving the request.
 8. A method for determining data usage access, the method comprising: identifying a current data usage quantity, the current data usage quantity associated with a mobile device; determining whether the current data usage quantity for an identified type of network connection is beyond a threshold data usage; responsive to the data usage quantity being beyond the threshold data usage, receiving mobile device information associated with the mobile device: analyzing the mobile device information; and determining access for the request based on the mobile device information and the current data usage quantity.
 9. The method of claim 8, wherein the mobile device information is at least one of: a timestamp; a location of the mobile device; the current data usage; a data usage history of the mobile device; a signal strength of the mobile device; a network connection availability of the mobile device; and a mobile device application usage trail.
 10. The method of claim 8, wherein the access is at least one of: allowing the request; prohibiting the request; deferring the request; and congesting the request.
 11. The method of claim 8, wherein the threshold data usage is based on a percentage of usage of a total data usage allotted for a billing cycle of a wireless subscription plan.
 12. The method of claim 8, wherein analyzing the mobile device information is based on mobile device information utilizing big data analytics.
 13. The method of claim 8, wherein the access comprises program instructions to prefetch the request when the mobile device is utilizing wireless connectivity.
 14. The method of claim 13, wherein prefetching the request comprises: receiving data usage history; analyzing the data usage history to yield a pattern of data usage activity; and downloading content based on the pattern of data usage activity prior to receiving the request.
 15. A method for determining data usage access, the method comprising: determining whether a current data usage quantity of a mobile device is beyond a threshold data usage, wherein the threshold data usage is determined at least based on a historical analysis of data usage for the mobile device in data increments over a period of time; responsive to the data usage quantity being beyond the threshold data usage, receiving mobile device information associated with the mobile device: analyzing the mobile device information; and determining access for the request based on the mobile device information and the current data usage quantity, wherein the access is determined without further interaction from a user.
 16. The method of claim 15, wherein the mobile device information is at least one of: a timestamp, a location of the mobile device, the current data usage, a data usage history of the mobile device, a signal strength of the mobile device, a network connection availability of the mobile device, and a mobile device application usage trail.
 17. The method of claim 15, wherein the access is at least one of: allowing the request, prohibiting the request, deferring the request, and congesting the request.
 18. The method of claim 15, wherein the threshold data usage is based on a percentage of usage of a total data usage allotted for a billing cycle of a wireless subscription plan.
 19. The method of claim 15, wherein analyzing the mobile device information is based on mobile device information utilizing big data analytics.
 20. The method of claim 15, wherein the access comprises prefetching the request when the mobile device is utilizing wireless connectivity. 